PathAGG

Multi-teacher pathology foundation students distilled with CRADIOv4-style aggregation from Virchow2, UNI2-h, and H1 (H-optimus-1).

Trained on ~66M TCGA + HISTAI patches (~1000 GPU-hours / student on 8× H100).
Code & EVA dumps: Luoxd1996/PathAGG.

Variant	subfolder	Embed	Official EVA avg (9 tasks)
ViT-B/14	vitb	768	79.57
ViT-S/14	vits	384	77.93

License: CC BY-NC 4.0 — non-commercial / research only.

Load (recommended)

pip install torch timm transformers torchvision Pillow

import torch
from PIL import Image
from torchvision import transforms
from transformers import AutoModel

preprocess = transforms.Compose([
    transforms.Resize(224, interpolation=transforms.InterpolationMode.BICUBIC),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])

model = AutoModel.from_pretrained(
    "luoxd96/PathAGG",
    subfolder="vitb",   # or "vits"
    trust_remote_code=True,
).eval().cuda()

img = Image.open("patch.png").convert("RGB")
x = preprocess(img).unsqueeze(0).cuda()

with torch.inference_mode():
    cls = model(x)                 # [1, 768] or [1, 384]
    cls, patch = model(x, return_patch=True)  # patch: [1, 256, D]

EVA results (%)

Task	Split	ViT-S	ViT-B
BreakHis	val	74.36	84.62
CRC	val	95.93	96.41
Gleason	val	78.72	77.33
MHIST	val	81.85	82.20
PCam	test	93.88	93.75
Cam16Small	test	83.59	85.03
PANDASmall	test	66.28	67.93
CoNSeP	val	63.58	64.04
MoNuSAC	val	63.22	64.82
Official Avg		77.93	79.57